Title:Towards Baselines for Shoulder Surfing on Mobile Authentication

Abstract: Given the nature of mobile devices and unlock procedures, unlock
authentication is a prime target for credential leaking via shoulder surfing, a
form of an observation attack. While the research community has investigated
solutions to minimize or prevent the threat of shoulder surfing, our
understanding of how the attack performs on current systems is less well
studied. In this paper, we describe a large online experiment (n=1173) that
works towards establishing a baseline of shoulder surfing vulnerability for
current unlock authentication systems. Using controlled video recordings of a
victim entering in a set of 4- and 6-length PINs and Android unlock patterns on
different phones from different angles, we asked participants to act as
attackers, trying to determine the authentication input based on the
observation. We find that 6-digit PINs are the most elusive attacking surface
where a single observation leads to just 10.8% successful attacks, improving to
26.5\% with multiple observations. As a comparison, 6-length Android patterns,
with one observation, suffered 64.2% attack rate and 79.9% with multiple
observations. Removing feedback lines for patterns improves security from
35.3\% and 52.1\% for single and multiple observations, respectively. This
evidence, as well as other results related to hand position, phone size, and
observation angle, suggests the best and worst case scenarios related to
shoulder surfing vulnerability which can both help inform users to improve
their security choices, as well as establish baselines for researchers.